{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#1f77b4\n",
      "#ff7f0e\n",
      "#2ca02c\n",
      "#d62728\n",
      "#9467bd\n",
      "#8c564b\n",
      "#e377c2\n",
      "#7f7f7f\n",
      "#bcbd22\n",
      "#17becf\n"
     ]
    }
   ],
   "source": [
    "from pylab import *\n",
    "import seaborn as sns\n",
    "# cmap = cm.get_cmap('seismic', 5)    # PiYG\n",
    "cmap = sns.color_palette(None, 10)\n",
    "\n",
    "for c in cmap:\n",
    "#     print (c)\n",
    "# for i in range(cmap.N):\n",
    "#     rgba = cmap(i)\n",
    "#     # rgb2hex accepts rgb or rgba\n",
    "    print(matplotlib.colors.rgb2hex(c))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from os.path import join\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "from os.path import dirname, realpath, join\n",
    "base_dir = dirname(dirname(os.getcwd()))\n",
    "sys.path.insert(0, base_dir)\n",
    "from config_path import PROSTATE_DATA_PATH, PLOTS_PATH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_dir = join(PROSTATE_DATA_PATH, 'raw_data')\n",
    "filename = '41588_2018_78_MOESM5_ESM.xlsx'\n",
    "data= pd.read_excel(join(base_dir, filename), skiprows=2, index_col=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cancer.Type.Detailed</th>\n",
       "      <th>Mutation_count</th>\n",
       "      <th>Mutation burden (Mutations per Megabase)</th>\n",
       "      <th>Normal Coverage</th>\n",
       "      <th>Tumor Coverage</th>\n",
       "      <th>Fraction of genome altered</th>\n",
       "      <th>Ploidy</th>\n",
       "      <th>Purity</th>\n",
       "      <th>Sample.Type</th>\n",
       "      <th>Metastatic.Site</th>\n",
       "      <th>Radical.Prostatectomy.Gleason.Score.for.Prostate.Cancer</th>\n",
       "      <th>Reviewed.Gleason.Category</th>\n",
       "      <th>Fusion</th>\n",
       "      <th>Age</th>\n",
       "      <th>Data.Source</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Patient.ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AAPC-STID0000011640-Tumor-SM-2XU1H</th>\n",
       "      <td>Prostate Adenocarcinoma</td>\n",
       "      <td>8</td>\n",
       "      <td>0.206976</td>\n",
       "      <td>87.13</td>\n",
       "      <td>86.01</td>\n",
       "      <td>0.010487</td>\n",
       "      <td>2.024373</td>\n",
       "      <td>0.216579</td>\n",
       "      <td>Primary</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>3+4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.0</td>\n",
       "      <td>AAPC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAPC-STID0000021561-Tumor-SM-3RVWB</th>\n",
       "      <td>Prostate Adenocarcinoma</td>\n",
       "      <td>35</td>\n",
       "      <td>0.838843</td>\n",
       "      <td>171.94</td>\n",
       "      <td>129.16</td>\n",
       "      <td>0.135831</td>\n",
       "      <td>1.969338</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>Primary</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>3+4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45.0</td>\n",
       "      <td>AAPC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAPC-STID0000011949-Tumor-SM-2XU1I</th>\n",
       "      <td>Prostate Adenocarcinoma</td>\n",
       "      <td>23</td>\n",
       "      <td>0.433527</td>\n",
       "      <td>102.92</td>\n",
       "      <td>69.41</td>\n",
       "      <td>0.190097</td>\n",
       "      <td>2.072929</td>\n",
       "      <td>0.492816</td>\n",
       "      <td>Primary</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&gt;=8</td>\n",
       "      <td>5+4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46.0</td>\n",
       "      <td>AAPC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAPC-STID0000021610-Tumor-SM-2XU13</th>\n",
       "      <td>Prostate Adenocarcinoma</td>\n",
       "      <td>19</td>\n",
       "      <td>0.364544</td>\n",
       "      <td>111.26</td>\n",
       "      <td>89.49</td>\n",
       "      <td>0.054238</td>\n",
       "      <td>2.091553</td>\n",
       "      <td>0.300396</td>\n",
       "      <td>Primary</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>3+3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.0</td>\n",
       "      <td>AAPC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAPC-STID0000021537-Tumor-SM-3RVW7</th>\n",
       "      <td>Prostate Adenocarcinoma</td>\n",
       "      <td>16</td>\n",
       "      <td>0.460031</td>\n",
       "      <td>106.96</td>\n",
       "      <td>108.37</td>\n",
       "      <td>0.054551</td>\n",
       "      <td>1.855555</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>Primary</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>3+4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.0</td>\n",
       "      <td>AAPC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       Cancer.Type.Detailed  Mutation_count  \\\n",
       "Patient.ID                                                                    \n",
       "AAPC-STID0000011640-Tumor-SM-2XU1H  Prostate Adenocarcinoma               8   \n",
       "AAPC-STID0000021561-Tumor-SM-3RVWB  Prostate Adenocarcinoma              35   \n",
       "AAPC-STID0000011949-Tumor-SM-2XU1I  Prostate Adenocarcinoma              23   \n",
       "AAPC-STID0000021610-Tumor-SM-2XU13  Prostate Adenocarcinoma              19   \n",
       "AAPC-STID0000021537-Tumor-SM-3RVW7  Prostate Adenocarcinoma              16   \n",
       "\n",
       "                                    Mutation burden (Mutations per Megabase)  \\\n",
       "Patient.ID                                                                     \n",
       "AAPC-STID0000011640-Tumor-SM-2XU1H                                  0.206976   \n",
       "AAPC-STID0000021561-Tumor-SM-3RVWB                                  0.838843   \n",
       "AAPC-STID0000011949-Tumor-SM-2XU1I                                  0.433527   \n",
       "AAPC-STID0000021610-Tumor-SM-2XU13                                  0.364544   \n",
       "AAPC-STID0000021537-Tumor-SM-3RVW7                                  0.460031   \n",
       "\n",
       "                                    Normal Coverage  Tumor Coverage  \\\n",
       "Patient.ID                                                            \n",
       "AAPC-STID0000011640-Tumor-SM-2XU1H            87.13           86.01   \n",
       "AAPC-STID0000021561-Tumor-SM-3RVWB           171.94          129.16   \n",
       "AAPC-STID0000011949-Tumor-SM-2XU1I           102.92           69.41   \n",
       "AAPC-STID0000021610-Tumor-SM-2XU13           111.26           89.49   \n",
       "AAPC-STID0000021537-Tumor-SM-3RVW7           106.96          108.37   \n",
       "\n",
       "                                    Fraction of genome altered    Ploidy  \\\n",
       "Patient.ID                                                                 \n",
       "AAPC-STID0000011640-Tumor-SM-2XU1H                    0.010487  2.024373   \n",
       "AAPC-STID0000021561-Tumor-SM-3RVWB                    0.135831  1.969338   \n",
       "AAPC-STID0000011949-Tumor-SM-2XU1I                    0.190097  2.072929   \n",
       "AAPC-STID0000021610-Tumor-SM-2XU13                    0.054238  2.091553   \n",
       "AAPC-STID0000021537-Tumor-SM-3RVW7                    0.054551  1.855555   \n",
       "\n",
       "                                      Purity Sample.Type Metastatic.Site  \\\n",
       "Patient.ID                                                                 \n",
       "AAPC-STID0000011640-Tumor-SM-2XU1H  0.216579     Primary             NaN   \n",
       "AAPC-STID0000021561-Tumor-SM-3RVWB  0.300000     Primary             NaN   \n",
       "AAPC-STID0000011949-Tumor-SM-2XU1I  0.492816     Primary             NaN   \n",
       "AAPC-STID0000021610-Tumor-SM-2XU13  0.300396     Primary             NaN   \n",
       "AAPC-STID0000021537-Tumor-SM-3RVW7  0.300000     Primary             NaN   \n",
       "\n",
       "                                   Radical.Prostatectomy.Gleason.Score.for.Prostate.Cancer  \\\n",
       "Patient.ID                                                                                   \n",
       "AAPC-STID0000011640-Tumor-SM-2XU1H                                                  7        \n",
       "AAPC-STID0000021561-Tumor-SM-3RVWB                                                  7        \n",
       "AAPC-STID0000011949-Tumor-SM-2XU1I                                                >=8        \n",
       "AAPC-STID0000021610-Tumor-SM-2XU13                                                  6        \n",
       "AAPC-STID0000021537-Tumor-SM-3RVW7                                                  7        \n",
       "\n",
       "                                   Reviewed.Gleason.Category Fusion   Age  \\\n",
       "Patient.ID                                                                  \n",
       "AAPC-STID0000011640-Tumor-SM-2XU1H                       3+4    NaN  40.0   \n",
       "AAPC-STID0000021561-Tumor-SM-3RVWB                       3+4    NaN  45.0   \n",
       "AAPC-STID0000011949-Tumor-SM-2XU1I                       5+4    NaN  46.0   \n",
       "AAPC-STID0000021610-Tumor-SM-2XU13                       3+3    NaN  48.0   \n",
       "AAPC-STID0000021537-Tumor-SM-3RVW7                       3+4    NaN  48.0   \n",
       "\n",
       "                                   Data.Source  \n",
       "Patient.ID                                      \n",
       "AAPC-STID0000011640-Tumor-SM-2XU1H        AAPC  \n",
       "AAPC-STID0000021561-Tumor-SM-3RVWB        AAPC  \n",
       "AAPC-STID0000011949-Tumor-SM-2XU1I        AAPC  \n",
       "AAPC-STID0000021610-Tumor-SM-2XU13        AAPC  \n",
       "AAPC-STID0000021537-Tumor-SM-3RVW7        AAPC  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnv= data['Fraction of genome altered']\n",
    "mut= data['Mutation_count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Patient.ID\n",
       "AAPC-STID0000011640-Tumor-SM-2XU1H    0.010487\n",
       "AAPC-STID0000021561-Tumor-SM-3RVWB    0.135831\n",
       "AAPC-STID0000011949-Tumor-SM-2XU1I    0.190097\n",
       "AAPC-STID0000021610-Tumor-SM-2XU13    0.054238\n",
       "AAPC-STID0000021537-Tumor-SM-3RVW7    0.054551\n",
       "Name: Fraction of genome altered, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnv.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "response_filename = 'response_paper.csv'\n",
    "processed_path = join(PROSTATE_DATA_PATH, 'processed')\n",
    "labels = pd.read_csv(join(processed_path, response_filename))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>response</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AAPC-STID0000011640-Tumor-SM-2XU1H</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAPC-STID0000021561-Tumor-SM-3RVWB</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAPC-STID0000011949-Tumor-SM-2XU1I</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAPC-STID0000021610-Tumor-SM-2XU13</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAPC-STID0000021537-Tumor-SM-3RVW7</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    response\n",
       "id                                          \n",
       "AAPC-STID0000011640-Tumor-SM-2XU1H         0\n",
       "AAPC-STID0000021561-Tumor-SM-3RVWB         0\n",
       "AAPC-STID0000011949-Tumor-SM-2XU1I         0\n",
       "AAPC-STID0000021610-Tumor-SM-2XU13         0\n",
       "AAPC-STID0000021537-Tumor-SM-3RVW7         0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = labels.set_index('id')\n",
    "labels.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "mut_df = mut.to_frame()\n",
    "cnv_df = cnv.to_frame()\n",
    "data = mut_df.join(cnv, how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# mut_df =mut_df.join(labels, how='inner')\n",
    "# mut_df = mut_df.join(cnv, how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mutation_count</th>\n",
       "      <th>Fraction of genome altered</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Patient.ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AAPC-STID0000011640-Tumor-SM-2XU1H</th>\n",
       "      <td>8</td>\n",
       "      <td>0.010487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAPC-STID0000021561-Tumor-SM-3RVWB</th>\n",
       "      <td>35</td>\n",
       "      <td>0.135831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAPC-STID0000011949-Tumor-SM-2XU1I</th>\n",
       "      <td>23</td>\n",
       "      <td>0.190097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAPC-STID0000021610-Tumor-SM-2XU13</th>\n",
       "      <td>19</td>\n",
       "      <td>0.054238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAPC-STID0000021537-Tumor-SM-3RVW7</th>\n",
       "      <td>16</td>\n",
       "      <td>0.054551</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Mutation_count  Fraction of genome altered\n",
       "Patient.ID                                                                    \n",
       "AAPC-STID0000011640-Tumor-SM-2XU1H               8                    0.010487\n",
       "AAPC-STID0000021561-Tumor-SM-3RVWB              35                    0.135831\n",
       "AAPC-STID0000011949-Tumor-SM-2XU1I              23                    0.190097\n",
       "AAPC-STID0000021610-Tumor-SM-2XU13              19                    0.054238\n",
       "AAPC-STID0000021537-Tumor-SM-3RVW7              16                    0.054551"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_traing_testing_sets():\n",
    "    traiing_file = join(PROSTATE_DATA_PATH,'splits/training_set.csv')\n",
    "    training_set = pd.read_csv(traiing_file, index_col=1)\n",
    "    test_file = join(PROSTATE_DATA_PATH,'splits/test_set.csv')\n",
    "    test_set = pd.read_csv(traiing_file, index_col=1)\n",
    "    return training_set, test_set\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_dataset(data, labels):\n",
    "    filtered = data.join(labels, how='inner')\n",
    "    y = filtered['response']\n",
    "    filtered = filtered[['Mutation_count','Fraction of genome altered']]\n",
    "    return filtered, y\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_set, test_set = get_traing_testing_sets()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, y_train = split_dataset(data, training_set)\n",
    "x_test, y_test = split_dataset(data, test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AAPC-STID0000011640-Tumor-SM-2XU1H    0\n",
       "AAPC-STID0000021561-Tumor-SM-3RVWB    0\n",
       "AAPC-STID0000011949-Tumor-SM-2XU1I    0\n",
       "AAPC-STID0000021610-Tumor-SM-2XU13    0\n",
       "AAPC-STID0000002520-Tumor-SM-2XTZB    0\n",
       "Name: response, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "model = Ridge()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None,\n",
       "   normalize=False, random_state=None, solver='auto', tol=0.001)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train.values, y_train.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = model.predict(x_test.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7753736046418973"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "auc = metrics.roc_auc_score(y_test, pred)\n",
    "auc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = '41588_2018_78_MOESM5_ESM.xlsx'\n",
    "base_dir = join(PROSTATE_DATA_PATH, 'raw_data')\n",
    "df= pd.read_excel(join(base_dir, filename), skiprows=2)\n",
    "\n",
    "df_mets= df[df['Sample.Type']=='Metastasis'].copy()\n",
    "df_primary = df[df['Sample.Type']=='Primary'].copy()\n",
    "\n",
    "mutations_mets = df_mets['Mutation_count']\n",
    "cnv_mets = df_mets['Fraction of genome altered']\n",
    "\n",
    "mutations_primary = df_primary['Mutation_count']\n",
    "cnv_primary = df_primary['Fraction of genome altered']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a723290>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "fig.set_size_inches(12, 8)\n",
    "\n",
    "plt.subplot(3,1,1)\n",
    "plt.plot(np.log(1+mutations_mets), cnv_mets, 'r.')\n",
    "# plt.xlabel('log(1+ # mutations)', fontsize=18)\n",
    "plt.title('Metastatic')\n",
    "plt.ylabel('CNA', fontsize=18)\n",
    "plt.xlim((0,8))\n",
    "plt.subplot(3,1,2)\n",
    "\n",
    "plt.plot(np.log(1+mutations_primary),cnv_primary , 'b.')\n",
    "# plt.xlabel('log(1+ # mutations)', fontsize=18)\n",
    "plt.title('Primary')\n",
    "plt.ylabel('CNA', fontsize=18)\n",
    "plt.xlim((0,8))\n",
    "plt.subplot(3,1,3)\n",
    "\n",
    "plt.scatter(np.log(1+mutations_mets), cnv_mets, edgecolors ='r', facecolors='none')\n",
    "plt.scatter(np.log(1+mutations_primary), cnv_primary, edgecolors= 'b', facecolors='none')\n",
    "plt.xlim((0,8))\n",
    "# plt.title('Primary and Metastatic')\n",
    "\n",
    "plt.xlabel('log(1+ # mutations)', fontsize=18)\n",
    "plt.ylabel('CNA', fontsize=18)\n",
    "plt.legend(['Primary', 'Metastatic'])\n",
    "saving_dir  = join(PLOTS_PATH, 'reviews')\n",
    "filename = join(saving_dir, 'mut_cn.png')\n",
    "plt.savefig(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a3026f590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "dl",
   "language": "python",
   "name": "dl"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
